A Weight-Based Channel Pruning Algorithm for Depth-Wise Separable Convolution Unit

Fan, Yizhe; Tang, Xiaoning; Ma, Zhijun

doi:10.1145/3508546.3508568

Cited by 2 publications

(1 citation statement)

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“…According to the different granularity, pruning methods can be divided into channel level, weight level, and hierarchy, which is a powerful tool to realize lightweight model. The channel-level pruning method [29,30] strikes a good balance between difficulty and flexibility for a variety of fully connected networks, allowing traditional convolutional neural networks (CNNs) to run quickly and efficiently on multiple platforms.…”

Section: Model Pruningmentioning

confidence: 99%

Weed Recognition at Soybean Seedling Stage Based on YOLOV8nGP + NExG Algorithm

Sun,

Cui,

Zong

et al. 2024

Agronomy

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The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and these require accurate distinction between crops and weeds. In this paper, images of soybean seedlings and weeds in different growth areas are used as datasets. In the aspect of soybean recognition, this paper designs a YOLOv8nGP algorithm with a backbone network optimisation based on GhostNet and an unconstrained pruning method with a 60% pruning rate. Compared with the original YOLOv8n, the YOLOv8nGP improves the Precision (P), Recall (R), and F1 metrics by 1.1% each, reduces the model size by 3.6 mb, and the inference time was 2.2 ms, which could meet the real-time requirements of field operations. In terms of weed recognition, this study utilises an image segmentation method based on the Normalized Excess Green Index (NExG). After filtering the soybean seedlings, the green parts of the image are extracted for weed recognition, which reduces the dependence on the diversity of the weed datasets. This study combines deep learning with traditional algorithms, which provides a new solution for weed recognition of soybean seedlings.

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Section: Model Pruningmentioning

confidence: 99%